Learning Outcomes
At the end of this course, students should be able to:
1. discuss biometric algorithms and data analysis along with digital image/signal processing;
2. apply automated biometric identification: hands-fingers, palms and hands; heads-face,
voice and eyes and other biometrics;
3. develop methods of obtaining biometric data and matching basics;
4. practice biometric authentication, enrolment, matching performance, setting a threshold.
biometric authentication, matching data, ground truth, calculating errors rates and graphs;
5. create storage of biometric data elements, quality, upgrades, data security and integrity;
6. analyse privacy issues, security strength, recognition rates and other aspects of biometrics,
passwords and smart cards; and
7. explore applications of biometrics and future trends.
Course Contents
Introduction to biometrics and digital image processing. Matlab in biometric image/signal
processing. Biometric algorithms and systems with emphasis on face, fingerprint, eyes (iris),
speech (voice). Automated biometric identification multimodal biometrics. Biometric data: raw
data, template data, and data methods. Biometric matching basics: biometric authentication,
enrolment, correct user, and incorrect user. Match threshold and matching performance.
Setting a threshold. Biometric authentication: matching data, ground truth, calculating errors
rates and graphs. Biometric data: Storage of biometric data elements, transactions, errors and
quality upgrades. Data security and integrity. Privacy issues and other aspects of biometrics.
Applications of biometrics and future trends. Challenging issues: security strength and
recognition rates. Alternatives of passwords and smart cards.
Lab work: Practical exercise on biometric capture, image processing, matching threshold and
performance. Learn the practical aspect of automated biometric identification of multimodal,
authentication and calculation of error rates. Work on biometric algorithms, privacy and
security of stored biometric data.
